论文标题
有效的数据融合与广义植被指数：农业土地覆盖分割的证据
Effective Data Fusion with Generalized Vegetation Index: Evidence from Land Cover Segmentation in Agriculture
论文作者
论文摘要
我们如何有效利用从遥感到卫星图像的更好细分农业土地覆盖范围的领域知识？在本文中，我们提出了一种针对植被相关的计算机视觉任务的新型，模型，数据融合方法。由域专家引入的各种植被指数（VIS）的动机，我们系统地审查了广泛用于遥感的VIS及其可行性，并将其可行性纳入深度神经网络。为了充分利用近红外通道，传统的红绿色蓝色通道以及植被指数或其变体，我们提出了一个广义植被指数（GVI），这是一个轻量级的模块，可以轻松地插入许多神经网络架构中，以作为其他信息输入。为了通过我们的GVI平滑训练模型，我们开发了一个添加群归一化（AGN）模块，该模块不需要规定的神经网络的额外参数。我们的方法使与植被相关的阶级的IOU阶级提高了0.9-1.3％，并在我们的基线上不断提高整体MIOU。
How can we effectively leverage the domain knowledge from remote sensing to better segment agriculture land cover from satellite images? In this paper, we propose a novel, model-agnostic, data-fusion approach for vegetation-related computer vision tasks. Motivated by the various Vegetation Indices (VIs), which are introduced by domain experts, we systematically reviewed the VIs that are widely used in remote sensing and their feasibility to be incorporated in deep neural networks. To fully leverage the Near-Infrared channel, the traditional Red-Green-Blue channels, and Vegetation Index or its variants, we propose a Generalized Vegetation Index (GVI), a lightweight module that can be easily plugged into many neural network architectures to serve as an additional information input. To smoothly train models with our GVI, we developed an Additive Group Normalization (AGN) module that does not require extra parameters of the prescribed neural networks. Our approach has improved the IoUs of vegetation-related classes by 0.9-1.3 percent and consistently improves the overall mIoU by 2 percent on our baseline.
